Event-based community creation for data sharing platform

ABSTRACT

Methods, computer-readable media, and processing systems are disclosed for establishing a data sharing community for an event of an event type. For example, a processing system including at least one processor may detect, via at least one data source device, a trigger condition for an event of an event type, and establish a data sharing community for the event, the data sharing community including a plurality of data sources, the plurality of data sources including the at least one data source device, and a plurality of data consumers, where the establishing includes setting respective permission levels for the data consumers. The processing system may further collect data from the plurality of data sources in accordance with the event type, and provide at least a first portion of the data to at least a first data consumer in accordance with a respective permission level of at least the first data consumer.

The present disclosure relates generally to network-based data storagesystems, and more particularly to methods, computer-readable media, andprocessing systems for establishing a data sharing community for anevent of an event type.

BACKGROUND

Many information systems involve a combination (join) of data sets frommultiple data owners. In one example, a requesting entity, such as adata analyst, may gain access to a single data set through an approvalchain. For example, the entity may declare a need for data X, and theowner of data X may grant access, e.g., after obtaining the entity'ssubmission to a non-disclosure agreement, various terms and conditions,etc. Access credentials may be provided to the entity, and then theentity may access the data, e.g., for a limited time until the accesscredentials expire. This may be repeated for different data setspossessed by different data owners. There may be some heuristics aroundthe timespans for having access to the data, the time periods covered bythe data, and hierarchical approvals.

SUMMARY

In one example, the present disclosure describes a method, computerreadable medium, and processing system for establishing a data sharingcommunity for an event of an event type. For instance, in one example, aprocessing system including at least one processor may detect, via atleast one data source device, a trigger condition for an event of anevent type, and establish a data sharing community for the event, thedata sharing community including a plurality of data sources, theplurality of data sources including the at least one data source device,and a plurality of data consumers, where the establishing includessetting respective permission levels for the plurality of dataconsumers. The processing system may further collect data from theplurality of data sources in accordance with the event type, and provideat least a first portion of the data from the plurality of data sourcesto at least a first data consumer of the plurality of data consumers inaccordance with a respective permission level of the at least the firstdata consumer.

BRIEF DESCRIPTION OF THE DRAWINGS

The teaching of the present disclosure can be readily understood byconsidering the following detailed description in conjunction with theaccompanying drawings, in which:

FIG. 1 illustrates an example system related to the present disclosure;

FIG. 2 illustrates an example system including a data sharing platform,according to the present disclosure;

FIG. 3 illustrates a flowchart of an example method for establishing adata sharing community for an event of an event type, in accordance withthe present disclosure; and

FIG. 4 illustrates an example high-level block diagram of a computingdevice specifically programmed to perform the steps, functions, blocks,and/or operations described herein.

To facilitate understanding, identical reference numerals have beenused, where possible, to designate identical elements that are common tothe figures.

DETAILED DESCRIPTION

The widespread availability of data among different public and privateentities provides numerous opportunities for improving general welfare.However, with data privacy concerns on the rise, and the civilresponsibility of government agencies being increasingly scrutinized,the ability to effectively share and distribute services is a continuouschallenge. In addition, some data sets lose long-term transparency withshort data lifecycles. Specifically, aggregated statistics may be lostor made inaccurate if the raw data expires without planning for futurere-use of summarized insights.

Examples of the present disclosure use permanent data sessions or “datasharing communities” within a data sharing platform for data recordingand responsible sharing for public welfare applications. In particular,a data sharing community may include data sources as well as dataconsumers (where a participant, also referred to as a “role” may be botha data source as well as a data consumer). A data sharing community mayhave an associated event type, and the data sharing community may beinstantiated when an event of the event type occurs. The data sharingcommunity may define what types of data may be shared from a pluralityof data sources, the format of the data, the shareable portions of thedata based on a particular time period or other criteria, and so forth.The data sharing community may also define permissions for which dataconsumers may access which data, for how long, and so forth.

In one example, data collected for a data sharing community can beautomatically aggregated, e.g., periodically, and the underlying rawdata deleted from the data sharing community and/or from the datasharing platform, facilitating both anonymized public services and asolution for long-term insight preservation. The data may therefore besearched by contextual priority and parallel data sets (e.g., medicalrecords, registration/property records, licensing records, criminalrecords, vital statistics, etc.) can be linked without fully decryptingthe raw data.

In one example, automated agents or bots may have increased roles andmore permissive access to data stored in the session (depending on whoowns the agent and/or the purpose of the agent). However, individualdata consumers may not need and/or may not be provided this role/levelof access. Some automated agents may be granted increased privilegesafter the initial establishment of a data sharing community by anothercivil entity that may be a member (e.g., for automated generation ofwarrants, death notices, etc.), or by a person of interest (e.g., aprivate citizen may choose to opt-in and share aspects of what may beconsidered his or her personal information).

Notably, a data session (e.g., a data sharing community) encompassingcivil entities, municipal systems (traffic control, Internet of Things(IoT), smart cities, etc.), and other stakeholders benefit from securityand immutability of records, and proper access restrictions/permissionsand role definitions. IoT and smart sensors can use the data session asa reliable (and authenticated) source for civil data. In addition,automated systems, e.g., for traffic management, may have automaticoverrides based on automatically observed events. Security mechanismsbuilt into the data session, such as privileges for restricted dataviewing, prevent unnecessary data accesses even among members of thedata sharing community. For example, a traffic signal controller shouldnot be able to access criminal records that may be associated with anaccident event, and may be restricted from such access by assigning arelevant permission level at the outset.

Examples of the present disclosure may provide for public review ofactions taken by various civic entities without endangering the abilityof such institutions to operate, may provide for synchronization andverification/authentication of control requests for municipal systemsand devices (e.g., traffic, utilities, etc.), may provide forcorrelation of different data sources in an access-controlled fashion byroles and permissions, may provide a reliable repository for large-formcontent (video, audio, etc.) that can be easily reconstructed andlogged, and may provide for preemptive, context-sensitive controls(time, location, critical need, etc.) for data access from a public datapool.

The data sharing platform may designate some data as public, other dataas private, and the rules of sharing and aging-out the data may bedefined by the platform in accordance with the rules of data sharingcommunities that are created (e.g., according to the event typetemplate(s)). Examples of the present disclosure also provide for publicresearchers to study and aggregate actions of civil entities across timeand location from direct data sources. For instance, the data sharingplatform may, in accordance with rules of a data sharing community,instantaneously log data relating to an event from IoT and smart citysensors, coordinate the IoT and smart city sensors in an authenticatedand auditable fashion, and efficiently and persistently store large-formdata as the data correlates with event(s) in an immutable fashion. Thedata sharing platform may further enable authenticated sharing of richcivic data (e.g., property records, criminal records, medical records,etc.) using role-managed data sessions. These and other aspects of thepresent disclosure are discussed in greater detail below in connectionwith the examples of FIGS. 1-4.

To aid in understanding the present disclosure, FIG. 1 illustrates ablock diagram depicting one example of an environment 100 suitable forperforming or enabling the steps, functions, operations, and/or featuresdescribed herein. As illustrated in FIG. 1, the environment 100 includesa telecommunication service provider network 110. In one example,telecommunication service provider network 110 may comprise a corenetwork, a backbone network or transport network, such as an InternetProtocol (IP)/multi-protocol label switching (MPLS) network, where labelswitched routes (LSRs) can be assigned for routing Transmission ControlProtocol (TCP)/IP packets, User Datagram Protocol (UDP)/IP packets, andother types of protocol data units (PDUs), and so forth. It should benoted that an IP network is broadly defined as a network that usesInternet Protocol to exchange data packets. However, it will beappreciated that the present disclosure is equally applicable to othertypes of data units and transport protocols, such as Frame Relay, andAsynchronous Transfer Mode (ATM). In one example, the telecommunicationservice provider network 110 uses a network function virtualizationinfrastructure (NFVI), e.g., host devices or servers that are availableas host devices to host virtual machines comprising virtual networkfunctions (VNFs). In other words, at least a portion of thetelecommunication service provider network 110 may incorporatesoftware-defined network (SDN) components.

In one example, telecommunication service provider network 110 isconnected to networks 114. The networks 114 may include a wirelessaccess network (e.g., an IEEE 802.11/Wi-Fi network and the like), a WideArea Network (WAN), a cellular access network, such a Universal MobileTelecommunications System (UMTS) terrestrial radio access network(UTRAN), an evolved UTRAN (eUTRAN), a base station subsystem (BSS),e.g., a Global System for Mobile communication (GSM) radio accessnetwork (GRAN), a 2G, 3G, 4G and/or 5G network, a Long Term Evolution(LTE) network, and the like), a circuit switched network (e.g., a publicswitched telephone network (PSTN)), a cable access network, a digitalsubscriber line (DSL) network, a metropolitan area network (MAN), othertypes of wired access networks, an Internet service provider (ISP)network, and the like. Alternatively, or in addition, networks 114 mayrepresent enterprise networks, corporate, governmental, or educationalinstitution LANs, a home/residential LAN, and the like. In oneembodiment, the networks 114 may all be different types of networks, mayall be the same type of network, or some networks may be of a same typeand others may be different types of networks. The access networks 114and the telecommunication service provider network 110 may be operatedby different service providers, the same service provider, or acombination thereof. For instance, in an example where networks 114include a cellular access network, telecommunication service providernetwork 110 may include evolved packet core (EPC) network components,network switching subsystem (NSS)/GSM core network and/or General PacketRadio Service (GPRS) core network components, and so forth. The networks114 (e.g., access networks) and the telecommunication service providernetwork 110 may be interconnected via one or more intermediary networks(not shown) which may utilize various different protocols andtechnologies for transporting communications in the form of datapackets, datagrams, protocol data units (PDUs), and the like, such asone or more IP/MPLS networks, one or more frame relay networks, one ormore ATM networks, and so forth. In one example, the networks 114 mayrepresent the Internet in general.

Further illustrated in FIG. 1 is one or more servers 112 intelecommunication service provider network 110. The server(s) 112 mayeach comprise all or a portion of a computing device or system, such ascomputing system 400, and/or processing system 402 as described inconnection with FIG. 4 below, specifically configured to perform varioussteps, functions, and/or operations for establishing a data sharingcommunity for an event of an event type, as described herein. Forexample, one of server(s) 112, or a plurality of servers 112collectively, may perform operations in connection with the examplemethod 300, or as otherwise described herein. In one example, the one ormore servers 112 may comprise a network-based processing system 210, asdescribed in greater detail below in connection with the example system200 of FIG. 2.

In addition, it should be noted that as used herein, the terms“configure,” and “reconfigure” may refer to programming or loading aprocessing system with computer-readable/computer-executableinstructions, code, and/or programs, e.g., in a distributed ornon-distributed memory, which when executed by a processor, orprocessors, of the processing system within a same device or withindistributed devices, may cause the processing system to perform variousfunctions. Such terms may also encompass providing variables, datavalues, tables, objects, or other data structures or the like which maycause a processing system executing computer-readable instructions,code, and/or programs to function differently depending upon the valuesof the variables or other data structures that are provided. As referredto herein a “processing system” may comprise a computing deviceincluding one or more processors, or cores (e.g., as illustrated in FIG.4 and discussed below) or multiple computing devices collectivelyconfigured to perform various steps, functions, and/or operations inaccordance with the present disclosure.

In one example, telecommunication service provider network 110 may alsoinclude one or more databases (DBs) 136, e.g., physical storage devicesintegrated with server(s) 112 (e.g., database servers), attached orcoupled to the server(s) 112, and/or in remote communication withserver(s) 112 to store various types of information in support ofsystems for establishing a data sharing community for an event of anevent type, as described herein. In one example, server(s) 112 and/orDB(s) 136 may comprise cloud-based and/or distributed data storageand/or processing systems comprising one or more servers at a samelocation or at different locations. For instance, DB(s) 136, or DB(s)136 in conjunction with one or more of the servers 112, may represent adistributed file system, e.g., a Hadoop® Distributed File System(HDFS™), or the like. In one example, DB(s) 136 in conjunction with oneor more of the servers 112, may comprise a data sharing platform 205, asdescribed in greater detail below in connection with the example system200 of FIG. 2.

In one example, DB(s) 136 may receive and store data for a variety ofdifferent data owners and from a variety of different data sourcedevices. The data source devices may include data source device 180,sensor(s) 156, traffic lights (or “traffic signals”) 152 and 154, datasource devices 132, vehicle 142, and so forth. To illustrate, DB(s) 136may receive information feeds from one or more data source device(s)132, such as a weather alert service, a traffic alert service, a publicsafety alert service, an aggregator alert service, and so on. Theinformation feeds may be in formats such as a SMS/text message-basedfeed, a Really Simple Syndication (RSS) feed, an email-based feed, andso forth. For instance, one of the data source devices 132 may representa weather data server (WDS). In one example, the weather data may bereceived by DB(s) 136 from the data source device(s) 132 via a weatherservice data feed, e.g., an NWS extensible markup language (XML) datafeed, or the like. In another example, the weather data may be obtainedby retrieving the weather data from the WDS. Similarly, one of the datasource devices 132 may represent a server of a traffic managementservice and may forward various traffic related data to DB(s) 136, suchas toll payment data, records of traffic volume estimates, trafficsignal timing information, and so forth. The information feeds may be informats such as a SMS/text message-based feed, a RSS feed, anemail-based feed, and so forth. The information feeds received by DB(s)136 may all be in a same format or may be in a plurality of differentformats.

Data source device(s) 132 may also represent systems providingrelatively static information to DB(s) 136, such as an insurancedatabase, a DMV driver database, demographic information of usersmaintained by a telecommunication network service provider, summaryhealth records, etc. In one example, DB(s) 136 may comprise a primarystorage system for data of various data owners. For instance,telecommunication service provider network 110 may store driver recordsin DB(s) on behalf of a civic entity, e.g., as a cloud storage provider.However, in another example, the data source device(s) 132 may compriseprimary storage devices for various static information, whereas a copy,or copies, of such information may be uploaded to DB(s) 136.

The DB(s) 136 may collect and store media content from data sourcedevice 180. For example, data source device 180 may include camera 191(e.g., a video camera, a camera to capture sequences of still images,etc.) and a microphone 194. The data source device 180 may thereforegenerate streams of media content comprising still images, audio, and/orvideo. In the present example, DB(s) 136 may receive media content fromdata source device 180 via one or more of the networks 114.

Similarly, DB(s) 136 may collect and store data from one or more sensors156. For instance, sensors 156 may include a wind speed sensorperiodically supplying wind speed measurements to DB(s) 136.Alternatively, or in addition, sensors 156 may collect measurementscontinuously, but may only report the measurements in response to arequest from DB(s) 136 and/or server(s) 112. For instance, sensors 156may collect and store 24 hours of measurement data and provide themeasurement data upon demand. In one example, DB(s) 136 may also collectand store data from traffic lights 152 and 154, e.g., light timinginformation. For example, traffic light may have variable light timingand it may be of interest which lights are red, green, yellow, flashing,blinking, etc. at particular times and/or for how long. It should benoted that in one example, traffic signal timing information may bereceived by DB(s) 136 from data source device(s) 132 comprisingserver(s) of a traffic management service. However, in another example,the traffic light timing information may be received from the trafficlights 152 and 154 without involvement of the data source device(s) 132.

In one example, vehicle 142 may also comprise a data source device. Forinstance, vehicle 142 may have an on-board processing system thatincludes a variety of components, such as a navigation system, adiagnostics system, an entertainment system, a dashboard camera, and soforth, as well as an antenna and wireless transceiver for wireless(e.g., cellular data) communications. Thus, vehicle 142 may also collecta variety of data that may be uploaded to DB(s) 136 via network(s) 114(e.g., a cellular access network).

In one example, a template for a data sharing community for events of aparticular event type may be created by agreement among the variousparties and may be stored by DB(s) 136. A template for a data sharingcommunity may include such thing as: at least one trigger condition forevents of the event type, a duration of time associated with a detectionof the at least one trigger condition for which data from the pluralityof data source devices is shareable, data fields of the data which areshareable in connection with events of the event type, permission levelsfor different data consumers/roles (e.g., the types of data, the datasources, the data fields, etc.) that are shareable in connection withevents of the event type, at least one retention time period forretaining the data from the plurality of data sources, an expirationcondition for the data sharing community, and so forth.

The parties may include owners of data source devices, such as an ownerof one of the data source device 180, sensor(s) 156, traffic lights 152and 154, data source devices 132, vehicle 142, an owner or custodian ofa data set comprising health information, an owner or custodian of adata set comprising demographic information of at least one individual,or an owner of a data set comprising personal information of at leastone individual. The parties may further include certain types of dataconsumers, such as a law enforcement entity, an emergency medicalresponse entity, a civil authority, an insurance entity, a medicalentity, a research entity, and so forth. Each of the parties maycomprise an accountholder or a stakeholder with respect to a datasharing platform that may be represented by server(s) 112 and DB(s) 136.Thus, a template for a data sharing community is created and may becomeactive in the data sharing platform when agreed to by the variousparties. Any one or more of the parties may define the characteristicsof the template for the data sharing community and provide thecharacteristics to DB(s) 136 and/or server(s) 112 for storage and/or foractivation of filtering for event detection and data sharing communitycreation.

The data consumers may be associated with one or more data consumerdevices 134. For example, data consumer devices 134 may each comprise adesktop computer and/or personal computer (PC), a mobile device, such asa cellular smart phone, a laptop, a tablet computer, etc., or aplurality or cluster of such devices. The data consumers may include atraffic management service (such as a state or local transportationauthority), a police, fire, or emergency medical service, an insuranceservice, and so on.

It should be noted that some of the data consumers may also comprisedata source device owners and/or data set owners. For instance, atraffic management service may control traffic lights 152 and 154, butmay also be a recipient of a video feed from vehicle 142 in connectionwith an event of the event type “traffic accident.” Data consumers mayalso comprise data source devices, such as vehicle 146, which maycomprise an ambulance with an on-board processing system that includes avariety of components, such as a navigation system, a diagnosticssystem, an entertainment system, a dashboard camera, and so forth, aswell as an antenna and wireless transceiver for wireless (e.g., cellulardata) communications. Thus, vehicle 146 may also collect a variety ofdata that may be uploaded to DB(s) 136 via network(s) 114 (e.g., acellular access network). Vehicle 146 may also receive a video feed ofvehicle 142, may receive medical summary information for an occupant ofvehicle 142, and so forth in connection with an event of the event type“traffic accident.”

In one example, DB(s) 136 may also store event detection filters (orevent signatures) for detecting triggering conditions for events ofparticular event types based upon data received from any one or moredata source devices (e.g., excluding relatively static data, such asdemographic information, driver records, health history, etc.). Theevent detection filters may be defined by one or more parties by consentand agreement in connection with creating the data sharing communitytemplate for events of the event type. In other words, the eventdetection filters may be created and stored as part of the template forthe event type. In one example, the event detection filters may comprisemachine learning algorithms (MLAs) trained on data from one or more datasources to identify events of a particular event type. For instance, afirst event detection filter may be for detecting vehicular collisionsin video feeds, a second event detection filter may be for detectingvehicular collisions from one or more information feeds, and so forth.

In one example, the one or more event detection filters (or eventsignatures) may be deployed by server(s) 112 to detect events based upondata received from any one or more data source devices. In one example,the server(s) 112 may process the data from data source device(s) beforestoring the data in DB(s) 136. Alternatively, or in addition, theserver(s) 112 may apply the event detection filters to the data alreadystored in DB(s) 136.

In an illustrative example, an event detection filter may be foridentifying traffic accidents in video feeds from cameras directed atroadways. However, it should be understood that events of the event type“traffic accident” may be detected in any number of ways using data fromany one or more data sources, e.g., from alerts from one or more of datasource devices 132 comprising an online crowd-sourced traffic incidentnotification application, from an RSS traffic incident data feed fromone or more of data source devices 132, and so forth, or using asignature based upon data from multiple sources, e.g., detection viaboth a video feed and via a traffic alert. For example, the video streamfrom camera 191 may include imagery of a crash between cars 140 and 142,while a text alert from one of data source devices 132 may state:“accident on 1-95 mile marker 82 reported at 11:15 am.”

Returning to the illustrative example of FIG. 1, the camera 191 may bedirected at a roadway 145 and may capture a video or a sequence ofimages which may be relayed to server(s) 112. In the present example,the video stream may include imagery of a crash between cars 140 and 142that may be detected by applying the event detection filter (e.g., aMLA-based detector). In one example, the MLA may comprise at least oneof: a deep neural network (DNN), a generative adversarial network (GAN),or the like. In one example, the machine learning algorithm may furtherinclude an exponential smoothing algorithm, (e.g., Holt-Winters tripleexponential smoothing) and/or a reinforcement learning algorithm. Itshould be noted that various other types of MLAs and/or MLMs may beimplemented in examples of the present disclosure, such as k-meansclustering and/or k-nearest neighbor (KNN) predictive models, supportvector machine (SVM)-based classifiers, e.g., a binary classifier and/ora linear binary classifier, a multi-class classifier, a kernel-basedSVM, etc., a distance-based classifier, e.g., a Euclidean distance-basedclassifier, or the like, and so on.

In any event, when a portion of a video stream is a match to the eventdetection filter, the server(s) 112 may then establish a data sharingcommunity for the event in accordance with the event type template. Forinstance, the server(s) 112 may begin to gather additional data fromvarious data sources (e.g., where the server(s) 112 were not previouslycollecting such data). Alternatively, or in addition, the server(s) 112may gather and store data from various data source devices on behalf ofvarious data owners on an ongoing basis. However, the server(s) 112 maythen begin to associate some of the previously stored data and/or newdata incoming from various data sources with the data sharing community.For instance, where the data may have previously been stored insegregated silos for different data owners, now all or selected portionsof the data of various respective data owners may now become availableto other participants in accordance with the template for the eventtype. As just one example, the event type template may includeinstructions for the server(s) 112 to collect video feed data fromvehicles that are detected to be within a certain distance from thelocation of the traffic accident and/or from the location of the camera191 via which the traffic accident is detected.

In addition, DB(s) 136 may store medical summary records for variousdrivers which may generally be stored in a manner that is inaccessibleto anyone but the drivers themselves. However, in accordance with theevent type template, the server(s) 112 may be authorized to make medicalsummary records of possible occupants of the vehicles 140 and 142available to one or more data consumers, such as an emergency medicalservice. It should be noted that the durations of video feeds, the typesof records, the fields of the records, the relevant time periods forvarious records, and so forth, may all be agreed upon, consented to, andspecified in the event type template. In one example, server(s) 112 mayapply data transformations to certain source data in accordance with theevent type template, such as blurring out non-subject vehicles orindividuals in a photograph or video, extracting relevant fields of adatabase and excluding unnecessary fields, such as educationalinformation from a demographic database when the event type is “trafficaccident.” In one example, server(s) 112 may copy relevant data fromrespective data silos of different data owners (e.g., stored in DB(s)136) into a common data environment for access by participants in thedata sharing community (e.g., also stored in DB(s) 136). However, inanother example, the data is not copied but is linked to the datasharing community and temporary permissions are granted by server(s) 112to itself.

In one example, notification of a possible event of the event type maybe provided to one or more data consumer devices 134. For example, thenotification may be sent in the form of an SMS/text message, an email,an RSS feed, etc. via networks 114, telecommunication service providernetwork 110, and so forth. In one example, server(s) 112 may alsoprovide access tokens (e.g., Kerberos tickets, or the like) to dataconsumer devices 134 to access data of the data sharing community. Forinstance, one of data consumer devices 134 may comprise an emergencymedical dispatcher who may receive a notification of a possible trafficaccident along with an access token. The particular one of the dataconsumer devices 134 may then access DB(s) 136 via server 112 to obtainadditional data of the data sharing community, e.g., to retrieve a videofeed from camera 191, to obtain traffic signal timing information fortraffic lights 152 and 154, and so on.

In one example, additional roles/participants may also be added afterthe initial establishment of the data sharing community. For example,the event type template may indicate that wireless communicationequipped vehicles that are opted in to this particular type of datasharing community participation and are detected to be within a certaindistance of camera 191 may also be enrolled in the data sharingcommunity as data source devices, data consumers, or both. For example,vehicle 146 may be enrolled to receive notifications of a possibletraffic accident ahead. However, the data sharing community may alsoreceive a video feed from a dashcam of vehicle 146 if so equipped. Thismay be useful to provide another vantage point of the traffic accident,to provide a view of possible traffic backup approaching the location ofcamera 191 and/or the location of the traffic accident, and so forth. Itshould be noted that the permission level(s) and hence the quantitiesand types of data of the data sharing community available to certaindata consumers (e.g., individual motorists) may be entirely differentfrom the permission level(s) of insurance services, which may further bedifferent from the permission level(s) of law enforcement entities, andso forth.

Alternatively, or in addition, the event type template may provide forobtaining more or less data from data source devices, and/or changingpermission levels for certain data consumers based upon a location. Forinstance, server(s) 112 may gather into the data sharing communityenvironment video data, audio data, vehicle monitoring data, and soforth for a vehicle detected to be involved in a traffic accident,whereas only video data may be gathered from an additional vehicle thatis approaching the location, but which is still somewhat far away.However, as the additional vehicle is detected to be closer than athreshold distance to the location of the traffic accident, theserver(s) 112 may then begin to also detect audio data from theadditional vehicle, speed data, and so forth.

Similarly, an on-board processing system of a vehicle of emergencyresponse personnel (e.g., vehicle 146) may be enrolled as a dataconsumer in the data sharing community and may be provided with accessto a first level of data comprising more basic information, such asvideo data from camera 191. However, as the vehicle 146 approachescloser to the scene of the traffic accident between vehicles 140 and142, the vehicle 146 may then be granted a second permission level whereadditional data of the data sharing community is accessible to thevehicle 146, such as medical summary information for individualsassociated with vehicles 140 and/or 142, vehicle status information ofvehicles 140 and/or 142 (e.g., to determine if there is a fuel leak, anengine is still running, etc.), and so forth. Thus, emergency medicalresponse personnel in a general area may all be enrolled as dataconsumers. However, emergency medical response personnel that appear toactually be responding to the scene may be granted additional access tomore substantial and useful information. In addition, certain dataconsumers and/or data source devices may be removed from the datasharing community prior to the end of the data sharing community. Forinstance, a vehicle passing a traffic accident may temporarily beenrolled as a data source device and/or a data consumer, but may beremoved from the data sharing community (e.g., all permissions revoked)when the vehicle is detected to be a threshold distance away from thelocation of camera 191 via which the traffic accident was detected inaccordance with the event detection filter.

In one example, the event type template may identify one or moreconditions for ending a data sharing community for an event of the eventtype. For instance, a first condition may specify a maximum duration ofthe data sharing community for a traffic accident, e.g., 12 hours, 24hours, 48 hours, etc. A second condition may specify that certainparticipants in the data sharing community may send an instruction toserver(s) 112 to end the data sharing community. For instance, a lawenforcement entity may be permitted to declare a data sharing communityto be ended. In still another example, the data sharing community mayend when all or a certain number of participants provide indications toserver(s) 112 that the data sharing community should be ended.

In yet another example, a second event detection filter may beassociated with a second event type and may be triggered at or near theend of the data sharing community associated with traffic accident. Forexample, the second event detection filter may detect that the datasharing community is to end, e.g., since a duration of time has passedsince the traffic accident was first detected, since one or moreparticipants have declared the data sharing community should end, and soon. However, this may also be a condition for creating the second datasharing community. For example, the first data sharing community mayprimarily meet the needs of motorists and emergency response personnelin ensuring safety and traffic flow. However, the second data sharingcommunity may be more directed to the administrative needs of themotorists involved in an accident, insurance providers, traffic safetyengineers, and so on.

As such, some or all of the data of the first data sharing community maybe transferred to the second data sharing community. However, certaindata may be omitted, transformed, e.g., summarized, and so forth. Forinstance, third-party drivers may not want the state or localtransportation authority to have open-ended access to dashcam videosfrom the vehicles of the respective third-party drivers. Thus, this datamay be excluded from the second data sharing community. However, thedata streams from vehicles 140 and 142 may be retained for insurancesettlement, for investigation by traffic safety engineers, etc. If anyentity still wants to review video feeds from vehicles of third-partydrivers, this data may still be requested. Nevertheless, since theurgency of the actual traffic accident has passed, certain entities arenot granted open-ended and unrestricted access to this data.

It should be noted that the environment 100 has been simplified. Inother words, the environment 100 may be implemented in a different formthan that illustrated in FIG. 1. For example, the environment 100 may beexpanded to include additional networks, and additional network elements(not shown) such as wireless transceivers and/or base stations, borderelements, routers, switches, policy servers, security devices, gateways,a network operations center (NOC), a content distribution network (CDN)and the like, without altering the scope of the present disclosure. Inaddition, environment 100 may be altered to omit various elements,substitute elements for devices that perform the same or similarfunctions and/or combine elements that are illustrated as separatedevices.

As just one example, the operations described above with respect toserver(s) 112 may alternatively or additionally be performed by adevice, or a plurality of devices coupled to network(s) 114. In oneexample, a first device may process data from data source devices todetect a trigger condition for an event of an event type, a seconddevice may create a data sharing community in accordance with an eventtype template, a third device may collect data from data sources, and soforth. In addition, an illustrative example is described in connectionwith a traffic accident event type. However, other, further, anddifferent examples may instead relate to event types such as a premisesbreak-in, overcrowding in a subway station, individuals or vehiclesapproaching an unmanned gate or other types of barricade, remotedetection of a particular type of wildlife, and so forth. Thus, theseand other modifications are all contemplated within the scope of thepresent disclosure.

FIG. 2 illustrates an example system 200 including a data sharingplatform 205 (e.g., a network-based data sharing platform). In oneexample, the data sharing platform 205 may comprise a processing system,e.g., a server or multiple servers collectively configured to performvarious steps, functions, and/or operations in accordance with thepresent disclosure. In one example, the data sharing platform 205includes a network based processing system 210, e.g., a server ormultiple servers collectively configured to perform various steps,functions, and/or operations in accordance with the present disclosure.It should also be noted that the components of network based processingsystem 210 and the data sharing platform 205 may comprise variouscombinations of computing resources (e.g., processor(s), memory unit(s),and/or storage unit(s)) on the same or different host devices, at thesame or different locations (e.g., in the same or different datacenters). For example, processors assigned to execute instruction setsfor different components may be separate from the associated memoryresources, which may be separate from associated storage resources wheredata sets or other data which may be processed via the differentcomponents may be stored, and so on.

As further illustrated in FIG. 2, the data sharing platform 205 includesa plurality of sandboxes 226-229 (e.g., “private sandboxes’) and apublic access application programming interface (API) gateway 240. Invarious examples, sandboxes 226-229, the data sets 281-284 stored in thedifferent sandboxes 226-229, and/or the public access API gateway 240,may comprise virtual machines, application containers, or the likeoperating on one or more host devices. In addition, sandboxes 226-229,the data sets 281-284 stored in the different sandboxes 226-229, and/orthe public access API gateway 240 may comprise various combinations ofcomputing resources, e.g., processor(s), memory unit(s), and/or storageunit(s) on one or more shared host devices and/or on separate hostdevices. Each of the data sets 281-284 may take a variety of differentforms, e.g., table-based records, video, audio, documents in variousformats, and so forth. However, for non-table based data sets, metadataregarding the various data/records may be maintained in table form. Inone example, the data sharing platform 205 may comprise a relationaldatabase system (RDBS). However, in other, further, and differentexamples, data sharing platform 205 may comprise a different type ofdatabase system, such as a hierarchical database system, a graph-baseddatabase system, etc.

The data sharing platform 205 may provide services to a number ofdifferent users, and interact with a number of user devices, such asdata owner devices 231-233 and data consumer devices 235. Each of theuser devices may comprise a desktop computer, a cellular smart phone, alaptop, a tablet computer, a cloud based processing system providing auser environment, and so forth. In particular, data sharing platform 205may be operated by a trusted party to store data sets on behalf of dataowners in a secure and restricted manner, and to provide data consumerswith data sharing community-based access to multiple data sets inaccordance with authorizations/permissions from data owners for eventsof one or more particular event types.

To illustrate, sandbox 226 may store data set 281 for a first dataowner, which may comprise medical information for various individuals.The data set 281 may include raw data (e.g., biometric sensor data)and/or may include data that is normalized, transformed, tagged, etc.(e.g., health summary records) before uploading to the data sharingplatform 205. In one example, the data in data set 281 may be uploadedvia data owner device 231 and stored in sandbox 226. Alternatively, orin addition, the data sharing platform 205 may be configured to obtainand/or receive the data comprising data set 281 directly from biometricsensors of various individuals (not shown). The sandbox 226 mayrepresent a secure data storage and data processing environment that isonly accessible to the first data owner (or another person or entityauthorized on behalf of the first data owner) and to the data sharingplatform 205.

Similarly, sandbox 227 may store data set 282 for a second data owner,which may comprise a vehicular on-board processing system managementservice. The data set 282 may include raw data and/or may include datathat is normalized, transformed, tagged, etc. before uploading to thedata sharing platform 205. In one example, the data in data set 282 maybe uploaded via data owner device 232 and stored in sandbox 227.Alternatively, or in addition, the data sharing platform 205 may beconfigured to obtain and/or receive the data comprising data set 282directly from various vehicular on-board processing systems (not shown).For instance, the data may include dashcam videos, engine diagnostics,entertainment system usage information, fuel status, brakinginformation, tire pressure information, and so forth. The sandbox 227may represent a secure data storage and data processing environment thatis only accessible to the second data owner (or another person or entityauthorized on behalf of the second data owner) and to the data sharingplatform 205.

In addition, sandbox 228 may store data set 283 for a third data owner,e.g., a traffic management service, which may comprise toll paymentdata, records of traffic volume estimates, traffic signal timinginformation, and so forth. The data set 283 may include raw data and/ormay include data that is normalized, transformed, tagged, etc. beforeuploading to the data sharing platform 205. In one example, the data indata set 283 may be uploaded via data owner device 233 and stored insandbox 228. Alternatively, or in addition, the data sharing platform205 may be configured to obtain and/or receive the data comprising dataset 283 directly from a traffic management system (not shown). Thesandbox 228 may represent a secure data storage and data processingenvironment that is only accessible to the third data owner (or anotherperson or entity authorized on behalf of the third data owner) and tothe data sharing platform 205.

In one example, data owners may make portions of data sets 281-283available to other users of the data sharing platform 205 or to thepublic at large via data sharing communities. For instance,network-based processing system 210 may run one or more event detectionfilters for detecting trigger conditions for one or more events. Thetrigger conditions may be detected via data of any one or more of thedata sets 281-283. In one example, owners of data sets 281-283 may grantpermission for the network-based processing system 210 to scan all or aportion of the data of the data sets 281-283 for such purposes. In oneexample, when a trigger condition is detected, network-based processingsystem 210 may create sandbox 229 for the new data sharing community. Inaddition, network-based processing system 210 may gather data from anyone or more of the data sets 281-283 and copy the data to the data set284 in sandbox 229. For example, the network-based processing system 210may access data sets 281-283 to obtain the relevant data, to filter,join, select columns, generate projections, and/or perform otheroperations in accordance with the event type template. In one example,the network-based processing system 210 is granted read-only access tothe data sets 281-283.

It should be noted that data in the data sets 281-283 may havetime-based rules for data expiration, data aggregation or summarization,or the like. However, in one example, the occurrence of an event, andhence the establishment of a data sharing community, may cause variousdata of the data sets 281-283 to be maintained in a particular formatand/or retained in the data sharing platform 205 for longer than wouldotherwise be the case. In one example, the network-based processingsystem 210 may also begin to gather new data from external data sourceswhen a data sharing community is established. For instance, data ownerdevice 233 may not typically upload data to the data sharing platform205. However, the owner of data owner device 233 may have agreed tocontribute data in connection with a particular data sharing community(e.g., when the trigger condition for establishing the data sharingcommunity is encountered). Thus, in one example, the network-basedprocessing system 210 may create sandbox 228 and begin populating dataset 283 with data from data owner device 233. This may be performed asan alternative or in addition to logging data into data set 284 ofsandbox 229 that is created exclusively for the data sharing communitywhen the data sharing community is initially established. In oneexample, the network-based processing system 210 may further applytransformations to the data in accordance with the event type template,e.g., identifying individuals, license plates, etc. in a photograph orvideo and blurring out or blocking faces, license plates, etc.,anonymizing fields in a database, excluding certain rows or columns ofdata, extracting records for certain time periods and omitting theremainder, and so forth.

In one example, network-based processing system 210 may also provide viathe public access API gateway 240 one or more tickets (e.g., a Kerberosticket, an X.509 certificate, or the like), to allow data consumerdevices 235 to access the sandbox 229 and/or data set 284 to retrievethe relevant data associated with the event. In one example,network-based processing system 210 may also push data from data set 284(e.g., data collected from data sets 281-283 and/or from external datasource devices of the data sharing community in accordance with theevent type template) to data consumer devices 235 in accordance with theevent type template. Alternatively, or in addition, data consumers, viadata consumer devices 235 may subscribe to certain aspects of data set284 when initially using a ticket to access the data sharing community(e.g., to access sandbox 229 and/or data set 284). For example, a firstdata consumer may want to receive any video feeds that are available inreal time while a second data consumer may want to receive biometricdata of certain individuals in real time.

It should also be noted that one or more of the data consumer devices235 may comprise an automated agent or bot, such as a device running anapplication (e.g., a MLA) for detecting certain conditions, forproviding alerts, notification, or control signals in response to suchconditions, and so forth. For example, one of data consumer devices 235may be able to detect vehicle license plates in images or videos, toperform image pattern matching to determine the license plates' states,to perform optical character recognition on detected license plates todetermine the license plate numbers, and so forth. Various additionaldata consumer devices 235 may comprise automated agents/bots of a sameor a similar nature. In addition, in one example, one or more of theconsumer devices 235 may also be enrolled as a data source device. Forexample, one of the consumer devices 235 for determining states andplate number of license plates may also provide the results back to thedata sharing community as additional data to be aggregated in data set284. Similarly, one or more of the data owner devices 231-233 may alsobe enrolled as both a data source device and a data consumer. Forinstance, an on-board processing system of a police vehicle maycontribute video data from a dashcam, and may also receive access todetected license plate information, medical information, and so forth asa data consumer, either by permitted access upon request, e.g., using anaccess token, and/or or via proactive push transfer of new data.

In one example, one or more of the data consumer devices 235 maycomprise an automated management system, e.g., for traffic signals,electrical power distribution, and so forth. For instance, in oneexample, one of the data consumer devices 235 may receive speedinformation from one or more vehicles on the same roadway as a trafficaccident and/or on nearby roadways, calculate the distance that trafficmay be backed-up on the same roadway, calculate a capacity of one ormore nearby roadways to alleviate the congestion, and control one ormore automated traffic signals (e.g., traffic lights, overhead textinformation screens, etc.) to reroute traffic. For instance, one of thedata consumer devices 235 may transmit a notification or instructions,or may otherwise cause a traffic signal to display an alert, to displaydetour instructions, etc. For instance, a traffic signal may change froma green light to a blinking yellow or red light to signal caution tomotorists. In another example, a traffic signal, e.g., a roadway sign,may display text such as “possible accident ahead—slow down and usecaution.” In still another example, a speed limit may be reduced in anarea near the incident. In one example, the traffic signals mayoriginate from one or more of the data owner devices 231-233.Alternatively, or in addition, the traffic signals may originate fromother devices (not shown) that do not contribute data to the datasharing community as data source devices.

It should also be noted that the example of FIG. 2 is provided only asan illustrative example. In other words, in other, further, anddifferent examples, the data sharing platform 205 may comprise adifferent architecture. For instance, operations that are described asbeing performing in connection with one component may alternatively oradditional be performed by a different component. In addition, while thedata sets 281-284 are illustrated as residing within sandboxes 226-229,it should be noted that the actual storage of data sets 281-284 may bedistributed in a plurality of different storage devices which may residewithin a plurality of different physical locations, where the sandboxes226-228 comprise environments where the respective data sets 281-283 canbe fully or partially accessed. For example, sandboxes 226-229 may eachrepresent at least a portion of a respective user application providedto data owner devices 231-233 via the data sharing platform 205. Forinstance, the user applications may run on network-based processors andmemory units of data sharing platform 205, where the sandboxes 226-228may possess security tokens (e.g., decryption keys) for rendering datasets 281-283, respectively. Thus, the storage locations of the data sets281-283 may be arbitrary, and the data owner devices 231-233 and dataconsumer devices 235 may interact with the data sets 281-284, performdata analysis, visualizations, and so forth via the respective userapplications hosted by the hardware of data sharing platform 205. In oneexample, data sets 281-284 may be part of a set of file stores such as aHadoop Distributed File System (HDFS) and/or another cloud file storagesystem. Thus, these and other variations, modifications, and/orenhancements, are all contemplated within the scope of the presentdisclosure.

FIG. 3 illustrates a flowchart of an example method 300 for providing anotification of an anomaly in a media content that is associated with anevent type. In one example, steps, functions and/or operations of themethod 300 may be performed by a device as illustrated in FIG. 1, e.g.,one or more of servers 112, or by a network-based processing system 210as illustrated in FIG. 2. Alternatively, or in addition, the steps,functions and/or operations of the method 300 may be performed by aprocessing system collectively comprising a plurality of devices asillustrated in FIG. 1 such as one or more of servers 112, DB(s) 136,data source devices 132 and 180, vehicle 142, traffic lights 152 and154, and so forth, or as illustrated in FIG. 2, such as network basedprocessing system 210, data sharing platform 205, data owner devices231-233, data consumer devices 235, and so forth. In one example, thesteps, functions, or operations of method 300 may be performed by acomputing device or processing system 400, and/or processor 402 asdescribed in connection with FIG. 4 below. In one example, the steps,functions, or operations of method 300 may be performed by a computingdevice or system 400, and/or a processing system 402 as described inconnection with FIG. 4 below. For instance, the computing device 400 mayrepresent at least a portion of a platform, a server, a system, and soforth, in accordance with the present disclosure. For illustrativepurposes, the method 300 is described in greater detail below inconnection with an example performed by a processing system. The method300 begins in step 305 and may proceed to step 310 or to optional step315.

At optional step 310, the processing system may create a template for anevent type in accordance with consent from a plurality of owners of aplurality of data sources to share data from the plurality of datasources in connection with events of the event type. The consent mayestablish the event type template, which may include at least onetrigger condition for events of the event type, a duration of timeassociated with a detection of the at least one trigger condition forwhich the data from the plurality of data sources is shareable, datafields of the data which are shareable in connection with events of theevent type, and permission levels for a plurality of data consumers inconnection with events of the event type. The consent may furtherestablish the event type template to include at least one of: at leastone retention time period for retaining the data from the plurality ofdata sources or an expiration condition for the data sharing community.The expiration condition may comprise a particular time, a duration oftime since the creation, or may comprise a signal from one or more ofthe data consumers (e.g., an authorized data consumer) to end the datasharing community. For example, a police entity may be authorized tosignal the end of the data sharing community.

At step 315, the processing system detects, via at least one data sourcedevice, a trigger condition for an event of an event type. In oneexample, the trigger condition may be based upon sensor data (e.g.,on/off, measurement value exceeded, etc.) or may be in accordance withone or more automated agents, e.g., a machine learning algorithm (MLA)to detect a traffic incident, the presence of a vehicle, the presence ofan individual, etc. from an image or video feed, to detect anenvironmental condition, e.g., from sensor data from a plurality ofsensors and/or from an image, sound, and/or video feed, and so forth. Inone example, the trigger condition may be in accordance with an eventtype template (e.g., an event type template that may be created atoptional step 310).

At step 320, the processing system establishes a data sharing communityfor the event. The data sharing community may comprise a plurality ofdata sources, e.g., including the at least one data source device viawhich the trigger condition is detected, and a plurality of dataconsumers. In one example, step 320 includes setting permission levelsfor the plurality of data consumers. In one example, the data sharingcommunity is established in accordance with an event type template(e.g., an event type template that may be created at optional step 310).The plurality of data sources may include, for example: at least onevideo source device, at least one audio source device, at least oneimage source device, at least one biometric sensor device, or at leastone environmental sensor device. The plurality of data sources mayalternatively or additionally include: at least one data set comprisinghealth information of at least one individual, at least one data setcomprising demographic information of at least one individual, or atleast one data set comprising personal information of at least oneindividual. In one example, the processing system may store databelonging to individual data owners/users. The processing system mayalso store personal information of a third-party data owner on behalf ofindividual users, e.g., a fitness tracking service provider, atelecommunications service provider, an insurance company, a motorvehicle commission, and so on.

In one example, the event type may comprise a traffic incident (e.g., atraffic accident, an abandoned object on a roadway, an animal on aroadway, and so on) and the plurality of data consumers may include atleast one of: a law enforcement entity, an emergency medical responseentity, a civil authority (e.g., a public or private organizationresponsible for a roadway associated with the traffic incident), aninsurance entity, a medical entity (e.g., a hospital, a doctor, anemergency medical technician (EMT), etc.), or a research entity (e.g., aresearcher interested in traffic management, highway safety, emergencymedical response outcomes, and so forth). In addition, in an examplewhere the event type comprises a traffic incident, the plurality of dataconsumers may include at least one of: a controllable roadway resource,a vehicular navigation system of at least one vehicle, or an automatedagent (e.g., an MLA) for processing the at least the first portion ofthe data. For example, an automated agent may be automatically joined toan event/data sharing community for various purposes, such as to verifythat a particular user's car is at a particular location, to notifyothers who may not be participants in the data sharing community of theoccurrence of the event via various means, such as a radio broadcastalert, a text message cellular broadcast alert, and so forth.

In one example, the establishing of the data sharing community maycomprise identifying at least one location of at least one of theplurality of data sources, and including the at least one of theplurality of data sources in the data sharing community when the atleast one location is within a threshold distance from a location of theevent. For example, a traffic incident may be detected via a firstcamera directed at a roadway. Then, additional cameras nearby that aredirected at the same roadway or nearby roadways may be enrolled as datasource devices in the data sharing community. However, available camerasthat might be included in other data sharing communities may be omittedfrom selection when deemed too far away from the event to be relevant.In one example, the threshold distance-based criteria for including adata source in the data sharing community applies to data source deviceswith active feeds, such as video cameras, biometric sensor devices,etc., but does not relate to data sources with relatively staticinformation, such as an insurance database, a driver database,demographic information of users maintained by a telecommunicationnetwork service provider, etc.

At step 325, the processing system collects data from the plurality ofdata sources in accordance with the event type (e.g., based upon atemplate for the event type that may be created at optional step 310).In one example, step 325 may include gathering data from the data sourcedevice via which the event is detected, and gathering data from one ormore additional data sources. In one example, step 325 may compriseretaining data from the plurality of data sources for a duration of timein excess of a default retention period. For example, if the processingsystem typically retains only 48 hours of video in accordance with theconfiguration of the data owner, the processing system may instead maykeep selected portions of the data for longer if deemed relevant to theevent (e.g., in accordance with the event type template and per theconsent of the data owner).

Alternatively, or in addition, step 325 may involve storing data thatwould not otherwise be stored. For example, a video camera may nottypically provide streaming video for storage by the processing system.However, according to the consent of the owner of the video camera, thevideo camera may be enrolled as a data source device for events of theevent type. Thus, there may be no data sharing in general for theparticular video camera, but this may be altered for traffic accidentswhile remaining in accordance with data owner restrictions (e.g., toonly retain until the event is ended, to anonymize portions of thevideo, etc.). In one example, the processing system is granted consentto store data sets on behalf of a plurality of owners of the pluralityof data sources and to access the data sets in accordance with the eventtype. In one example, the data from the plurality of data sources isstored by the processing system in separate data sets for each of theplurality of owners of the plurality of data sources, where each of theseparate data sets is accessible to a respective one of the plurality ofowners and is inaccessible to others of the plurality of owners.

In one example, step 325 may comprise anonymizing at least a firstportion of the data from the plurality of data sources. For instance,when the at least the first portion of the data comprises images or avideo, the anonymizing may comprise obfuscating at least a portion ofthe images or the video, such as blurring or blocking out vehicles,license plates, people who are not related to the event, children, etc.In one example, the anonymizing may include obfuscating data (e.g.,randomizing within a range), or deleting or omitting certain fields,rows, columns, entries, etc. For instance, the at least the firstportion of the data may comprise personally identifiable information(e.g., a name, an email, a social security number, a driver's licensenumber, a birthdate, etc.), demographic information (such as gender,height, weight, race, age, education, income, assets, etc.), orbiometric information (such as heart rate, temperature, blood pressure,skin conductance, etc.).

At step 330, the processing system provides at least a first portion ofthe data from the plurality of data sources to at least a first dataconsumer of the plurality of data consumers in accordance with apermission level of the at least the first data consumer. In oneexample, the processing system may provide the at least the firstportion of the data upon receiving a request from the first dataconsumer. Alternatively, or in addition, the processing system mayprovide the at least the first portion of the data to the first dataconsumer according to a push notification model. In one example, atleast one of the data sources may also be one of the data consumers,e.g., an in-vehicle computing system may provide dashcam video footage,and the in-vehicle computing system may also receive data from theprocessing system as a data consumer, e.g., to aid in navigating aroundthe event.

At optional step 335, the processing system may receive, from the atleast the first data consumer, a request for at least a second portionof the data of the plurality of data sources. For example, the at leastthe second portion of the data may comprise data that is not shareablein accordance with the permission level of the at least the first dataconsumer at the time of the providing of the at least the first portionof the data.

At optional step 340, the processing system may verify a condition forproviding the at least the second portion of the data to the at leastthe first data consumer. For example, the condition may comprise adistance of the at least the first data consumer to a location of theevent. To illustrate, an event type template for the event may indicatethat general information may be provided to all EMT, fire, and/or policeentities within a given range, but may provide more detailed informationto personnel who actually respond to the incident and are detected to beat or very near to the location, such as blood type information, drivingrecords, arrest records, current biometric information of vehicleoccupants, pedestrians, or bystanders at the scene, etc.

In one example, the condition may comprise receiving an affirmation fromthe at least the first data consumer of an emergency situation. Forexample, consent from data owners may provide for limited sharing for anevent. However, when an emergency condition exists, the data owners mayallow more open sharing, but only when an affirmation (which isrecordable and reproducible) is provided by a data consumer that isauthorized to make such an affirmation (and to request and to receivethe at least the second portion of the data). For instance, EMT, police,and fire personnel may get blood type information, biometricinformation, and other health information in an emergency, but thisinformation may not similarly be released to an insurance company, othermotorists on the roadway, etc. via the processing system. Likewise, aninsurance company may not be authorized to declare an emergency and maynot receive this information even when one of the other data consumersdoes declare an emergency.

At optional step 345, the processing system may provide the at least thesecond portion of the data to the at least the first data consumer inresponse to the verifying the condition. In one example, the processingsystem may establish a push notification model to provide a feedcomprising the at least the second portion of the data to the at leastthe first data consumer. For instance, the at least the first dataconsumer may be provided, at a device of the at least the first dataconsumer, a video feed, a biometric data feed, etc. in real-time (e.g.,at the speed at which the processing system is capable of obtaining theat least the second portion of the data and providing the at least thesecond portion of the data to the device, accounting for normalprocessing delays, network delay, etc.).

At optional step 350, the processing system may detect a second triggercondition for establishing a second data sharing community for the eventof the event type. For example, the second trigger condition maycomprise receiving declarations from one or more of the data consumersthat the event ended, e.g., via a message to the processing system via auser interface of a device of the one or more of the data consumers. Inanother example, the second trigger condition may comprise detectingthat a traffic incident has been resolved, e.g., detectingsmooth-flowing traffic at a normal/average speed per vehicle, etc.

At optional step 355, the processing system may establish the seconddata sharing community, where the second data sharing community mayinclude at least a second data consumer. In one example, the processingsystem may collect and/or store aggregate data derived from the at leasta first portion of the data from the plurality of data sources. Thus,the aggregate data may be accessible to the at least the second dataconsumer via the second data sharing community. In one example, the datacan be anonymized to be shared for additional public study, such asstatistics regarding what happened, demographics of involved parties,etc. For example, the processing system may create the second datasharing community to publish the aggregate data for study by one or moreresearchers. However, it should be noted that in one example, theestablishment of the second data sharing community does not necessarilylimit the publication of anonymized aggregate data to a public data set.

Following step 330, or optional steps 345 or 355, the method 300 mayproceed to step 395. At step 395, the method 300 ends.

It should be noted that the method 300 may be expanded to includeadditional steps or may be modified to include additional operationswith respect to the steps outlined above. For example, the method 300may be expanded to include repeating steps 325 and 330 through multipleiterations. In another example, the method 300 may be expanded toinclude receiving a request for additional data of one or more datasources, submitting the request to one or more of the data owners,receiving authorizations to release the data, and providing the data tothe requesting data consumer in accordance with the permission. In stillanother example, the method 300 may be expanded to include ending thefirst data sharing community upon one or more conditions. It should benoted that when the first data sharing community ends, data retained inconnection therewith may be deleted, or the data may be protected for atime and then declassified. After the event, devices such ascontrollable roadway resources may then be put back into configurationsas before the event. In addition, data collection and/or data retentionmay revert to default settings for one or more of the data sources.Thus, these and other modifications are all contemplated within thescope of the present disclosure.

In addition, it should be noted that although not specificallyspecified, one or more steps, functions or operations of the method 300may include a storing, displaying and/or outputting step as required fora particular application. In other words, any data, records, fields,and/or intermediate results discussed in the respective methods can bestored, displayed and/or outputted to another device as required for aparticular application. Furthermore, steps or blocks in FIG. 3 thatrecite a determining operation or involve a decision do not necessarilyrequire that both branches of the determining operation be practiced. Inother words, one of the branches of the determining operation can bedeemed as an optional step. In addition, one or more steps, blocks,functions, or operations of the above described method 300 may compriseoptional steps, or can be combined, separated, and/or performed in adifferent order from that described above, without departing from theexample embodiments of the present disclosure.

Furthermore, the capturing and dissemination of any of the capturedvideo, audio, and/or other data are only performed in full compliancewith the pertinent privacy rules and policies that are in effect at thetime. In other words, the captured likenesses, identities, personalinformation, and so forth of any individuals would only be done with thepermission of the individuals (e.g., opting-into a service with fullnotice of the potential actions of capturing and dissemination of suchdata) or as permitted by law.

FIG. 4 depicts a high-level block diagram of a computing device orprocessing system specifically programmed to perform the functionsdescribed herein. As depicted in FIG. 4, the processing system 400comprises one or more hardware processor elements 402 (e.g., a centralprocessing unit (CPU), a microprocessor, or a multi-core processor), amemory 404 (e.g., random access memory (RAM) and/or read only memory(ROM)), a module 405 for establishing a data sharing community for anevent of an event type, and various input/output devices 406 (e.g.,storage devices, including but not limited to, a tape drive, a floppydrive, a hard disk drive or a compact disk drive, a receiver, atransmitter, a speaker, a display, a speech synthesizer, an output port,an input port and a user input device (such as a keyboard, a keypad, amouse, a microphone and the like)). In accordance with the presentdisclosure input/output devices 406 may also include antenna elements,transceivers, power units, and so forth. Although only one processorelement is shown, it should be noted that the computing device mayemploy a plurality of processor elements. Furthermore, although only onecomputing device is shown in the figure, if the method 300 as discussedabove is implemented in a distributed or parallel manner for aparticular illustrative example, i.e., the steps of the above method300, or the entire method 300 is implemented across multiple or parallelcomputing devices, e.g., a processing system, then the computing deviceof this figure is intended to represent each of those multiple computingdevices.

Furthermore, one or more hardware processors can be utilized insupporting a virtualized or shared computing environment. Thevirtualized computing environment may support one or more virtualmachines representing computers, servers, or other computing devices. Insuch virtualized virtual machines, hardware components such as hardwareprocessors and computer-readable storage devices may be virtualized orlogically represented. The hardware processor 402 can also be configuredor programmed to cause other devices to perform one or more operationsas discussed above. In other words, the hardware processor 402 may servethe function of a central controller directing other devices to performthe one or more operations as discussed above.

It should be noted that the present disclosure can be implemented insoftware and/or in a combination of software and hardware, e.g., usingapplication specific integrated circuits (ASIC), a programmable gatearray (PGA) including a Field PGA, or a state machine deployed on ahardware device, a computing device or any other hardware equivalents,e.g., computer readable instructions pertaining to the method discussedabove can be used to configure a hardware processor to perform thesteps, functions and/or operations of the above disclosed method 300. Inone example, instructions and data for the present module or process 405for establishing a data sharing community for an event of an event type(e.g., a software program comprising computer-executable instructions)can be loaded into memory 404 and executed by hardware processor element402 to implement the steps, functions, or operations as discussed abovein connection with the illustrative method 300. Furthermore, when ahardware processor executes instructions to perform “operations,” thiscould include the hardware processor performing the operations directlyand/or facilitating, directing, or cooperating with another hardwaredevice or component (e.g., a co-processor and the like) to perform theoperations.

The processor executing the computer readable or software instructionsrelating to the above described method can be perceived as a programmedprocessor or a specialized processor. As such, the present module 405for establishing a data sharing community for an event of an event type(including associated data structures) of the present disclosure can bestored on a tangible or physical (broadly non-transitory)computer-readable storage device or medium, e.g., volatile memory,non-volatile memory, ROM memory, RAM memory, magnetic or optical drive,device or diskette, and the like. Furthermore, a “tangible”computer-readable storage device or medium comprises a physical device,a hardware device, or a device that is discernible by the touch. Morespecifically, the computer-readable storage device may comprise anyphysical devices that provide the ability to store information such asdata and/or instructions to be accessed by a processor or a computingdevice such as a computer or an application server.

While various examples have been described above, it should beunderstood that they have been presented by way of illustration only,and not a limitation. Thus, the breadth and scope of any aspect of thepresent disclosure should not be limited by any of the above-describedexamples, but should be defined only in accordance with the followingclaims and their equivalents.

What is claimed is:
 1. A method comprising: detecting, by a processingsystem including at least one processor via at least one data sourcedevice, a first trigger condition for an event of an event type;establishing, by the processing system in response to the detecting ofthe first trigger condition, a first data sharing community for theevent, wherein the first data sharing community comprises a plurality ofdata sources and a plurality of data consumers, wherein at least one ofthe plurality of data sources also comprises at least one of theplurality of data consumers, wherein the establishing includes:identifying at least one location of the at least one of the pluralityof data sources; including the at least one of the plurality of datasources in the first data sharing community when the at least onelocation is within a threshold distance from a location of the event;and setting respective permission levels for the plurality of dataconsumers, wherein the plurality of data sources includes: the at leastone data source device, wherein the at least one data source devicecomprises at least one video source device; at least one data setcomprising health information of at least one individual; and at leastone data set comprising personal information of the at least oneindividual; collecting, by the processing system, data from theplurality of data sources in accordance with the event type; providing,by the processing system, at least a first portion of the data from theplurality of data sources to at least a first data consumer of theplurality of data consumers in accordance with a respective permissionlevel of the at least the first data consumer; detecting, by theprocessing system, a second trigger condition for establishing a seconddata sharing community for the event of the event type, wherein thesecond trigger condition is different from the first trigger condition;and establishing, by the processing system in response to the detectingof the second trigger condition, the second data sharing community,wherein the second data sharing community includes at least a seconddata consumer, wherein aggregate data derived from the at least thefirst portion of the data from the plurality of data sources isaccessible to the at least the second data consumer via the second datasharing community, and wherein the at least the first portion of thedata from the plurality of data sources is excluded from access by thesecond data sharing community, wherein the second data sharing communitydiffers from the first data sharing community by at least one consumer,and wherein the second data sharing community is established after thefirst data sharing community.
 2. The method of claim 1, wherein thecollecting comprises retaining the data from the plurality of datasources for a duration of time in excess of a default retention period.3. The method of claim 1, wherein the processing system is granted aconsent to store data sets on behalf of a plurality of owners of theplurality of data sources and to access the data sets in accordance withthe event type.
 4. The method of claim 1, further comprising: creatingan event type template in accordance with a consent from a plurality ofowners of the plurality of data sources to share the data from theplurality of data sources in connection with additional events of theevent type.
 5. The method of claim 4, wherein the consent establishes:at least one trigger condition for the additional events of the eventtype, the at least one trigger condition including the triggercondition; a duration of time associated with a detection of the atleast one trigger condition for which the data from the plurality ofdata sources is shareable; data fields of the data which are shareablein connection with the additional events of the event type; and therespective permission levels for the plurality of data consumers inconnection with the additional events of the event type.
 6. The methodof claim 5, wherein the consent further establishes at least one of: atleast one retention time period for retaining the data from theplurality of data sources; or an expiration condition for the first datasharing community.
 7. The method of claim 4, wherein the data from theplurality of data sources is stored by the processing system in separatedata sets for each of the plurality of owners of the plurality of datasources, wherein each of the separate data sets is accessible to arespective one of the plurality of owners and is inaccessible to othersof the plurality of owners.
 8. The method of claim 7, wherein thecollecting further comprises: anonymizing the at least the first portionof the data from the plurality of data sources.
 9. The method of claim8, wherein the at least the first portion of the data comprises stillimages or a video, wherein the anonymizing comprises: obfuscating atleast a portion of the still images or the video.
 10. The method ofclaim 1, wherein the plurality of data sources further includes: atleast one audio source device; at least one image source device; atleast one biometric sensor device; at least one environmental sensordevice; or at least one data set comprising demographic information ofthe at least one individual.
 11. The method of claim 1, wherein theevent type comprises a traffic incident, wherein the plurality of dataconsumers includes at least one of: a law enforcement entity; anemergency medical response entity; a civil authority; an insuranceentity; a medical entity; or a research entity.
 12. The method of claim1, wherein the event type comprises a traffic incident, wherein theplurality of data consumers includes at least one of: a controllableroadway resource; an on-board processing system of at least one vehicle;or a machine learning algorithm for processing the at least the firstportion of the data.
 13. The method of claim 1, further comprising:receiving, from the at least the first data consumer, a request for atleast a second portion of the data of the plurality of data sources;verifying a condition for providing the at least the second portion ofthe data to the at least the first data consumer; and providing the atleast the second portion of the data to the at least the first dataconsumer in response to the verifying the condition.
 14. The method ofclaim 13, wherein the at least the second portion of the data is notshareable in accordance with the respective permission level of the atleast the first data consumer at a time of the providing the at leastthe first portion of the data.
 15. The method of claim 13, wherein thecondition comprises a distance of the at least the first data consumerto a location of the event.
 16. The method of claim 13, wherein thecondition comprises receiving an affirmation from the at least the firstdata consumer of an emergency situation.
 17. A non-transitorycomputer-readable medium storing instructions which, when executed by aprocessing system including at least one processor, cause the processingsystem to perform operations, the operations comprising: detecting, viaat least one data source device, a first trigger condition for an eventof an event type; establishing, in response to the detecting of thefirst trigger condition, a first data sharing community for the event,wherein the first data sharing community comprises a plurality of datasources and a plurality of data consumers, wherein at least one of theplurality of data sources also comprises at least one of the pluralityof data consumers, wherein the establishing includes: identifying atleast one location of the at least one of the plurality of data sources;including the at least one of the plurality of data sources in the firstdata sharing community when the at least one location is within athreshold distance from a location of the event; and setting respectivepermission levels for the plurality of data consumers, wherein theplurality of data sources includes: the at least one data source device,wherein the at least one data source device comprises at least one videosource device; at least one data set comprising health information of atleast one individual; and at least one data set comprising personalinformation of the at least one individual; collecting data from theplurality of data sources in accordance with the event type; providingat least a first portion of the data from the plurality of data sourcesto at least a first data consumer of the plurality of data consumers inaccordance with a respective permission level of the at least the firstdata consumer; detecting a second trigger condition for establishing asecond data sharing community for the event of the event type, whereinthe second trigger condition is different from the first triggercondition; and establishing, in response to the detecting of the secondtrigger condition, the second data sharing community, wherein the seconddata sharing community includes at least a second data consumer, whereinaggregate data derived from the at least the first portion of the datafrom the plurality of data sources is accessible to the at least thesecond data consumer via the second data sharing community, and whereinthe at least the first portion of the data from the plurality of datasources is excluded from access by the second data sharing community,wherein the second data sharing community differs from the first datasharing community by at least one consumer, and wherein the second datasharing community is established after the first data sharing community.18. A device comprising: a processing system including at least oneprocessor; and a computer-readable medium storing instructions which,when executed by the processing system, cause the processing system toperform operations, the operations comprising: detecting, via at leastone data source device, a first trigger condition for an event of anevent type; establishing, in response to the detecting of the firsttrigger condition, a first data sharing community for the event, whereinthe first data sharing community comprises a plurality of data sourcesand a plurality of data consumers, wherein at least one of the pluralityof data sources also comprises at least one of the plurality of dataconsumers, wherein the establishing includes: identifying at least onelocation of the at least one of the plurality of data sources; includingthe at least one of the plurality of data sources in the first datasharing community when the at least one location is within a thresholddistance from a location of the event; and setting respective permissionlevels for the plurality of data consumers, wherein the plurality ofdata sources includes: the at least one data source device, wherein theat least one data source device comprises at least one video sourcedevice; at least one data set comprising health information of at leastone individual; and at least one data set comprising personalinformation of the at least one individual; collecting data from theplurality of data sources in accordance with the event type; providingat least a first portion of the data from the plurality of data sourcesto at least a first data consumer of the plurality of data consumers inaccordance with a respective permission level of the at least the firstdata consumer; detecting a second trigger condition for establishing asecond data sharing community for the event of the event type, whereinthe second trigger condition is different from the first triggercondition; and establishing, in response to the detecting of the secondtrigger condition, the second data sharing community, wherein the seconddata sharing community includes at least a second data consumer, whereinaggregate data derived from the at least the first portion of the datafrom the plurality of data sources is accessible to the at least thesecond data consumer via the second data sharing community, and whereinthe at least the first portion of the data from the plurality of datasources is excluded from access by the second data sharing community,wherein the second data sharing community differs from the first datasharing community by at least one consumer, and wherein the second datasharing community is established after the first data sharing community.19. The device of claim 18, wherein the collecting comprises retainingthe data from the plurality of data sources for a duration of time inexcess of a default retention period.
 20. The device of claim 18,wherein the device is granted a consent to store data sets on behalf ofa plurality of owners of the plurality of data sources and to access thedata sets in accordance with the event type.